An Efficient Hierarchical Stereo Matching Approach Combining Daisy-LBP Descriptor with Regional Plane Constraints

An Efficient Hierarchical Stereo Matching Approach Combining Daisy-LBP Descriptor with Regional Plane Constraints | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article An Efficient Hierarchical Stereo Matching Approach Combining Daisy-LBP Descriptor with Regional Plane Constraints xuetao zheng, jingxue wang, xiao xie This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7311791/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Although significant advancements have been made in stereo vision over the past decade, reliable depth estimation remains a challenge, especially in regions with repetitive or poor textures and at depth discontinuities. To address these challenges, an efficient hierarchical stereo matching approach is introduced, leveraging the strengths of the PatchMatch filter (PMF) and a multi-resolution pyramid strategy. Firstly, a stereo image pyramid is constructed using downsampling parameters, and each layer is segmented by the Simple Linear Iterative Clustering (SLIC) algorithm to build a superpixel-level adjacency graph. Secondly, a robust cost function is designed by integrating the Daisy-LBP descriptor and image gradients, based on which a hierarchical multi-resolution cost volume is constructed within the pyramid framework. Thirdly, a hierarchical stereo matching strategy is embedded into a robust PMF algorithm. The strategy begins processing the lowest-resolution image pair, applying cross-based local multipoint filtering (CLMF) for cost aggregation and refining pixel-wise disparity planes using regional plane constraints. At higher-resolution layers, disparity planes are initialized using the optimal results from the preceding layer. This iterative process continues until it reaches the original-resolution image pair. Lastly, the robust PMF algorithm integrates multi-resolution cost aggregation with prior disparity planes at the original resolution to further improve robustness in poor-texture and repetitive regions. Experimental evaluations conducted with the Middlebury and KITTI benchmarks demonstrate that the proposed approach enhances both matching accuracy and robustness, particularly in challenging regions and at depth discontinuities, outperforming comparative methods. Physical sciences/Engineering Physical sciences/Mathematics and computing Depth estimation Daisy-LBP descriptor Stereo matching Regional Plane constraint Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 19 May, 2026 Reviews received at journal 23 Jan, 2026 Reviewers agreed at journal 09 Jan, 2026 Reviewers agreed at journal 29 Sep, 2025 Reviewers invited by journal 29 Sep, 2025 Editor invited by journal 11 Aug, 2025 Editor assigned by journal 09 Aug, 2025 Submission checks completed at journal 08 Aug, 2025 First submitted to journal 06 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7311791","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":527084372,"identity":"11f754c0-cc56-41e1-817d-ec15f1a18f3b","order_by":0,"name":"xuetao zheng","email":"","orcid":"","institution":"Liaoning Technical University","correspondingAuthor":false,"prefix":"","firstName":"xuetao","middleName":"","lastName":"zheng","suffix":""},{"id":527084373,"identity":"4a3c6b8a-35ed-49ba-add4-b0354b1d131f","order_by":1,"name":"jingxue 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Constraints","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Depth estimation, Daisy-LBP descriptor, Stereo matching, Regional Plane constraint","lastPublishedDoi":"10.21203/rs.3.rs-7311791/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7311791/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAlthough significant advancements have been made in stereo vision over the past decade, reliable depth estimation remains a challenge, especially in regions with repetitive or poor textures and at depth discontinuities. To address these challenges, an efficient hierarchical stereo matching approach is introduced, leveraging the strengths of the PatchMatch filter (PMF) and a multi-resolution pyramid strategy. Firstly, a stereo image pyramid is constructed using downsampling parameters, and each layer is segmented by the Simple Linear Iterative Clustering (SLIC) algorithm to build a superpixel-level adjacency graph. Secondly, a robust cost function is designed by integrating the Daisy-LBP descriptor and image gradients, based on which a hierarchical multi-resolution cost volume is constructed within the pyramid framework. Thirdly, a hierarchical stereo matching strategy is embedded into a robust PMF algorithm. The strategy begins processing the lowest-resolution image pair, applying cross-based local multipoint filtering (CLMF) for cost aggregation and refining pixel-wise disparity planes using regional plane constraints. At higher-resolution layers, disparity planes are initialized using the optimal results from the preceding layer. This iterative process continues until it reaches the original-resolution image pair. Lastly, the robust PMF algorithm integrates multi-resolution cost aggregation with prior disparity planes at the original resolution to further improve robustness in poor-texture and repetitive regions. Experimental evaluations conducted with the Middlebury and KITTI benchmarks demonstrate that the proposed approach enhances both matching accuracy and robustness, particularly in challenging regions and at depth discontinuities, outperforming comparative methods.\u003c/p\u003e","manuscriptTitle":"An Efficient Hierarchical Stereo Matching Approach Combining Daisy-LBP Descriptor with Regional Plane Constraints","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-10 05:12:31","doi":"10.21203/rs.3.rs-7311791/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"209195780070051462494996321479085966957","date":"2026-05-19T13:49:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-23T08:37:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"188243471744510553511450645253205190631","date":"2026-01-09T10:54:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"209195780070051462494996321479085966957","date":"2025-09-29T12:15:54+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-29T11:36:31+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-12T03:53:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-09T05:38:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-08T04:27:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-08-06T16:22:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"976b7521-c3d9-43f5-9f10-b84475953b11","owner":[],"postedDate":"October 10th, 2025","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"209195780070051462494996321479085966957","date":"2026-05-19T13:49:09+00:00","index":117,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":56025369,"name":"Physical sciences/Engineering"},{"id":56025370,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2025-10-10T05:12:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-10 05:12:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7311791","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7311791","identity":"rs-7311791","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}